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Citation: Kleineidam, J.

Distinguishing Organisational

Profiles of Food Loss Management in

Logistics. Logistics 2022,6, 61.

https://doi.org/10.3390/

logistics6030061

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Gaudêncio Mendonça Freires and

Lucila Maria de Souza Campos

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logistics

Article

Distinguishing Organisational Profiles of Food Loss

Management in Logistics

Julia Kleineidam

Institute for Technology and Management, Berlin University of Technology, 10623 Berlin, Germany;

[email protected]

Abstract:

Background: Food loss management (FLM), which is discussed at length in the literature,

lacks a scientific basis on which to determine the current engagement of actors in the food value

chain and what is relevant to derive appropriate measures according to the circumstances in the

organisations concerned. Therefore, this paper aims to derive patterns by which the engagement

of actors can be distinguished and, on this basis, to make recommendations for further action.

Methods: Based on an online survey of 40 participants, a clustering analysis was conducted using the

unsupervised learning method and hierarchical clustering (R and R Studio). Results: Five clusters

representing different profiles were derived, showing how actors in the food value chain have

addressed FLM in the past. The derived profiles do not represent stages of development but rather

characteristics of organisations that have addressed FLM in a certain way in the past. Conclusions:

For the five organisational profiles, recommendations for action were given for further engagement

with FLM. As the level of engagement with FLM increases, organisations should tackle increasingly

complex measures to reduce food losses. At the same time, a shift in measures from the tactical to the

strategic planning level was derived.

Keywords:

food loss management; logistics and supply chain management; SMEs; clustering;

online survey

1. Introduction

A growing awareness of sustainable consumption has been observed among con-

sumers for some time [

]. This awareness relates not only to, for example, regionality and

reduction in CO

emissions, but also increasingly to the demand for food value chains

to have as few losses as possible [

]. This trend is also reflected in the goals of the global

community, which specifically stated in Target 12.3 of the Sustainable Development Goals

of the United Nations that food losses should be halved worldwide [

]. Although this

target suggests that there is a clear picture of the amount of loss, such information is not

available. Accurate data are lacking due to a lack of awareness among stakeholders and the

fact that neither a standard for recording losses nor a uniform definition of the delimitation

of losses has been established [

]. The Food and Agriculture Organization of the United

Nations and the United Nations Environment Program have taken the first steps towards

outlining a clearer quantitative picture of global food losses and waste with the Food

Loss Index and the Food Waste Index by compiling many publicly available sources with

estimates and surveys [

]. In recent years, several scientists have investigated various

measures that can contribute to the reduction in lifecycle losses in logistics processes [

–

particularly the implementation of technological solutions that have the potential to reduce

food losses [9–14].

However, these significant steps do not provide a complete picture that companies

and other actors in the food value chain can use to guide their food loss management

(FLM) activities.

Logistics 2022,6, 61. https://doi.org/10.3390/logistics6030061 https://www.mdpi.com/journal/logistics

Logistics 2022,6, 61 2 of 23

Minimal focus is being placed on how organisations in food value chains in general

are currently addressing FLM and how measures against food losses are selected on the

basis of these conditions. Without an understanding of the extent to which organisations

have already gained experience in this area, it is difficult to develop measures tailored to

the given circumstances in an organisation.

Nonetheless, to address the issue and provide food value chain actors with a way to

determine where they stand, a qualitative readiness assessment model of an organisation’s

FLM was developed [

]. Based on this model, the question arose as to the actual readiness

level of actors in the food value chain in practice. Furthermore, on this basis, it was

determined on which fields of action of FLM in logistics these actors should focus their

activities on the best case, which resulted in the following research questions:

RQ1:

How can organisations of food value chains be differentiated in terms of their readi-

ness levels with regard to the implementation of FLM in logistics?

RQ2:

How can this distinction be used to derive recommendations for action for the future

implementation of FLM in logistics?

To answer these questions, the theoretical background is presented below. Subse-

quently, the methodology used for data collection via an online survey and the data

analyses using a cluster analysis are described. The five resulting clusters are analysed

and explained in the results section. The implication section derives recommendations for

action for the company characteristics identified in the clusters.

2. Theoretical Background

This section presents the theoretical background needed to understand the research

presented in this paper.

2.1. Food Loss Management in Logistics

Food logistics is defined as:

‘the planning, management and control of the food value network from the source of raw

materials to the customer. The focus is on the efficient processing of customer orders with

the help of information systems, technologies and management concepts, with special

consideration of high item-specific quality and safety requirements of perishable goods in

different temperature ranges’ [2] (p. 6)

‘Food loss’ is a term for which there are many definitions in the literature, especially

when distinguished from the term ‘food waste’ [

–

]. This paper follows the under-

standing of the High-Level Panel of Experts (HLPE) on Food Security and Nutrition of the

European Union, which defines food loss as the loss of mass food that was intended for

human consumption at all stages of the food chain before it reaches the end consumer. By

contrast, food waste is defined as the loss of mass food intended for human consumption

at the end consumer stage [20].

The following hierarchy (highest to lowest) was developed with regard to the ways

in which losses can be addressed: avoidance of losses, reuse (e.g., through food banks),

recycling (e.g., further processing into animal feed or composting), recovery (e.g., losses are

used for energy generation) and disposal, which should be avoided if possible [21].

Logistics 2022,6, 61 3 of 23

Table 1. Description of the food loss management fields of action [22].

Field of Action Description

Transparency Increase transparency (exchange data and information) within the

organisation and between organisations of a network.

Quality management Improve early detection of weaknesses.

Packaging management

Improve transport and storage processes as well as distribution to the

end customer.

Transport optimisation Improve route planning and loading and coordination of vehicles.

Warehouse management Improve use of suitable storage equipment, storage strategies and

adapted layout planning.

Network structure Improve strategic network planning and location management.

Physical characteristics

Process adaptations designed to fit the specific physical requirements

of the product.

Shelf-life optimisation

Adapt processes to consider special physical requirements of the

products, including temperature, pressure sensitivity and

air composition.

Network cooperation

Improve cooperation within networks, including information sharing

and efforts to develop long-term business relationships.

Mindfulness

Promote awareness among employees at all levels in the organisation

regarding the importance of process efficiency and reducing food

losses in everyday life.

Consumer satisfaction Adapt internal processes with the aim of meeting specific

customer requirements.

Regulation Adapt regulations that affect the food value chain and encourage

active players to reduce losses.

Financing opportunities Financial support measures for active actors in the food value chain

that enable them to reduce losses.

Following this understanding, the definition of FLM in logistics was derived as follows:

The organisation of preventive measures within the planning, management and con-

trol of the value-added network of food as well as the organisation of re-use, recycling

and recovery measures. These measures can have a strategic, tactical or operational

character and can be assigned to the following 13 fields of action: transparency, quality

management, packaging management, transport optimisation, warehouse management,

network structure, physical characteristics, shelf-life optimisation, network cooperation,

mindfulness, consumer satisfaction, regulation and financing opportunities. (Table 1)

Various activities with different objectives can be carried out within these fields of

action. Considering the most important aspects relevant in logistics, these include measures

that contribute to the overall improvement of quality, shorten the time to consumers and/or

aim to optimise costs while considering the reduction in food losses.

The framework conditions are primarily the responsibility of administrative and

regulatory authorities, which are not the focus of this study; therefore, these fields of action

are not analysed further.

The fields of action of FLM can also be categorised in terms of management level (strate-

gic, tactical, operational) and application level (internal, cross-organisational) (

see Figure 1).

Logistics 2022,6, 61 4 of 23

Logistics 2022, 6, x FOR PEER REVIEW 4 of 23

Figure 1. Classification of the fields of action according to application and company level [22].

2.2. Structure of Food Value Chains

Today’s food value chains are often characterised by complex structures and many

actors.

By definition, measures undertaken by the end consumer are not included in this

work. While the first group of actors is farmers, not all of their activities are discussed in

this study. For example, work in the field that leads to losses cannot be directly assigned

to FLM in logistics. However, all operational processes of storage and food transport car-

ried out by the farmer belong to it. Thus, this group of actors was considered in FLM in

logistics. The process of post-harvesting is also partly carried out by farmers, but it is also

carried out by logistics service providers or intermediaries [23–26].

In international logistics chains in particular, import and export companies are the

next actors to handle food. They are usually responsible for the control of cross-border

transport, even if they are operationally carried out by logistics service providers. If the

consumption or production takes place in the country of origin, the transport carried out

by local service providers is the next step in the chain. For products sent for further pro-

cessing, production follows regardless of the current location. Losses that occur directly

through production processes are not the direct focus of FLM in logistics, but processes

that are part of production logistics are included. Further transport, whether local or in-

ternational, follows production to access the consumer markets [25,27,28].

The food then reaches the end customers via markets or wholesalers (retailers, res-

taurants and caterers). Authorities and regulatory authorities also influence food value

chains through guidelines and regulations that influence FLM [16,17,25]. Furthermore,

especially in developing countries, NGOs are important actors in the food value chain.

Depending on the local situation, they take on different tasks and are thus actors who

have to carry out FLM. The structure of the food value chain outlined here is shown in

Figure 2.

Quality management Transparency

Transport optimisation

Warehouse

management

Network structure

Packaging

management

Consumer satisfaction

Shelf-life optimisation

Physical characteristics

Mindfulness

Network cooperation

Strategic level

Operational level

Internal Cross-organisational

Figure 1. Classification of the fields of action according to application and company level [22].

2.2. Structure of Food Value Chains

Today’s food value chains are often characterised by complex structures and

many actors.

By definition, measures undertaken by the end consumer are not included in this

work. While the first group of actors is farmers, not all of their activities are discussed in

this study. For example, work in the field that leads to losses cannot be directly assigned to

FLM in logistics. However, all operational processes of storage and food transport carried

out by the farmer belong to it. Thus, this group of actors was considered in FLM in logistics.

The process of post-harvesting is also partly carried out by farmers, but it is also carried

out by logistics service providers or intermediaries [23–26].

In international logistics chains in particular, import and export companies are the

next actors to handle food. They are usually responsible for the control of cross-border

transport, even if they are operationally carried out by logistics service providers. If the

consumption or production takes place in the country of origin, the transport carried out by

local service providers is the next step in the chain. For products sent for further processing,

production follows regardless of the current location. Losses that occur directly through

production processes are not the direct focus of FLM in logistics, but processes that are

part of production logistics are included. Further transport, whether local or international,

follows production to access the consumer markets [25,27,28].

The food then reaches the end customers via markets or wholesalers (retailers, restau-

rants and caterers). Authorities and regulatory authorities also influence food value chains

through guidelines and regulations that influence FLM [

]. Furthermore, especially

in developing countries, NGOs are important actors in the food value chain. Depending on

the local situation, they take on different tasks and are thus actors who have to carry out

FLM. The structure of the food value chain outlined here is shown in Figure 2.

Logistics 2022,6, 61 5 of 23

Logistics 2022, 6, x FOR PEER REVIEW 5 of 23

Figure 2. Functional areas and actors in international food value chains, own representation based

on [16,17,22].

After introducing the scope of the study, the following section presents how actors

in the food value chain can approach FLM.

2.3. Readiness Assessment in Food Loss Management

The discussion of logistical processes includes the following five dimensions: em-

ployees, network, technology, strategy and process [29].

Under this structure, presented in Figure 3, the activities of an organisation in the

food value chain were examined. At the employee level, the extent to which they were

regularly offered training on the reduction in losses was considered, along with the extent

to which relevant information on food loss was made available to decision makers, and

whether incentive systems were established to encourage employees to contribute ideas

for the reduction in food losses.

Figure 2.

Functional areas and actors in international food value chains, own representation based

on [16,17,22].

After introducing the scope of the study, the following section presents how actors in

the food value chain can approach FLM.

2.3. Readiness Assessment in Food Loss Management

The discussion of logistical processes includes the following five dimensions: employ-

ees, network, technology, strategy and process [29].

Under this structure, presented in Figure 3, the activities of an organisation in the

food value chain were examined. At the employee level, the extent to which they were

regularly offered training on the reduction in losses was considered, along with the extent

to which relevant information on food loss was made available to decision makers, and

whether incentive systems were established to encourage employees to contribute ideas for

the reduction in food losses.

At the network level, both the strength of cooperation and joint work with network

partners on projects to reduce losses were considered. In addition, the extent to which

agreements existed with network partners on how losses were dealt with between partners

was investigated.

The technology level considered how organisations introduced new technologies to re-

duce food losses, the extent to which technology was used to achieve loss transparency, and

the extent to which technical interfaces were used to communicate with

network partners.

The strategy level examined how much knowledge about losses was available in

the organisation’s processes, whether and to what extent the organisation made financial

commitments to reduce the losses of living resources, and the extent to which management

demonstrated the importance of FLM.

The consideration of process levels included the sensitivity of forecasting, the de-

fined processes regarding the handling of food losses and the documentation of FLM

processes [15].

Logistics 2022,6, 61 6 of 23

Logistics 2022, 6, x FOR PEER REVIEW 6 of 23

Figure 3. Readiness assessment model of food loss management in logistics [15].

At the network level, both the strength of cooperation and joint work with network

partners on projects to reduce losses were considered. In addition, the extent to which

agreements existed with network partners on how losses were dealt with between part-

ners was investigated.

The technology level considered how organisations introduced new technologies to

reduce food losses, the extent to which technology was used to achieve loss transparency,

and the extent to which technical interfaces were used to communicate with network part-

ners.

The strategy level examined how much knowledge about losses was available in the

organisation’s processes, whether and to what extent the organisation made financial

commitments to reduce the losses of living resources, and the extent to which manage-

ment demonstrated the importance of FLM.

The consideration of process levels included the sensitivity of forecasting, the defined

processes regarding the handling of food losses and the documentation of FLM processes

[15].

3. Research Design

To identify the readiness levels of actors within the food value chain, a hierarchical

clustering method was applied using underlying data drawn from an online survey. Due

to the resources available and the given circumstances of the COVID-19 pandemic (which

made face-to-face interviews impossible), an online survey using the Unipark platform

was chosen for data collection.

The cluster analysis used is an exploratory data analysis technique based on inter-

pretation by a researcher who has insight into the original data [30]. The method provides

the possibility of finding connections in the data with the help of machine learning, which

Readiness Assessment Model of Food Loss Management

Employees

Training processes for all levels of employees

Providing information for qualified decision making

Incentive systems

Network

Strength of current cooperation with direct network partners

Dealing with losses between network partners

Joint projects to reduce FL

Technology

Use of technology to create transparency of information

Introduction of new technologies to improve processes

Useful technical interfaces to network partners

Strategy

Knowledge of the scale of FL

Financial commitment to reduce FL

Demonstrated relevance of FLM by management

Processes

sensible forecasting process

Defined processes for dealing with FL

Documentation of FLM processes

Figure 3. Readiness assessment model of food loss management in logistics [15].

3. Research Design

To identify the readiness levels of actors within the food value chain, a hierarchical

clustering method was applied using underlying data drawn from an online survey. Due

to the resources available and the given circumstances of the COVID-19 pandemic (which

made face-to-face interviews impossible), an online survey using the Unipark platform

was chosen for data collection.

The cluster analysis used is an exploratory data analysis technique based on interpre-

tation by a researcher who has insight into the original data [

]. The method provides

the possibility of finding connections in the data with the help of machine learning, which

might be immediately self-evident, and an approach to classifying or grouping observa-

tions; thus, it delivers the results necessary for the research questions [

]. The method

was suitable in the present use case because the database contained many different variables

for which the identification of commonalities and patterns would be difficult for humans

to discern, and which corresponds directly to the strengths of cluster analysis.

In the following section, the data collection and analysis are described in detail.

3.1. Data Collection

To conduct the cluster analysis, a questionnaire was developed, based on the system

described above, to measure the readiness level of FLM in logistics [

]. Using the above-

mentioned dimensions of the model, statements were formulated for which an assessment

was made on a seven-point Likert scale as to the extent to which this statement applied

to the respective structures. The statements were derived from the above theoretical

background and modified by feedback from colleagues [29].

Four statements were developed for the employee level:

Logistics 2022,6, 61 7 of 23

•

The organisation’s employees are regularly trained in food handling and food

loss prevention;

•

The organisation’s employees are regularly provided with data on the volume of food

losses within the organisation to create transparency;

•

Employees with decision-making authority are provided with all necessary informa-

tion regarding food losses to support decision making;

•

Employees are motivated to make suggestions for process improvements that will

lead to a reduction in food losses through an incentive system.

Five statements were formulated for the network level:

•

We provide our direct network partners with information about our food losses on a

regular basis;

•

Our direct network partners provide us with information about their food losses on a

regular basis;

•

If we receive spoiled/inappropriate food products from a supplier, we have agreed

upon processes with our suppliers regarding how to deal with these food products;

•

If our customers receive spoiled/inappropriate food from us, we have agreed upon

processes with our customers regarding how to deal with these food products;

•

We work with our network partners on joint projects to reduce losses across organisa-

tional boundaries.

Four statements were developed for the technology level:

•Information technologies are used efficiently to collect data on food losses;

•

Information technologies are used efficiently to increase transparency regarding

food losses;

•

The organisation regularly reviews new technologies for their potential to improve

the organisation’s FLM;

•

Electronic data interfaces have been established with network partners to share food

loss data.

Seven statements were created for the strategy level:

•We have an exact overview of losses in our processes and can quantify them;

•

We have an accurate overview of the financial expenses that cause food losses and can

quantify them;

•

Management leads by making FLM a key strategic issue and supporting continuous

improvement;

•The organisation’s specific FLM objectives and requirements are properly defined;

•

FLM activities are included in the organisation’s business plans, and continuous

improvement tools are defined;

•

For FLM, an organisational structure that demands and utilises the full potential of

the workforce is implemented;

•

Targeted communication is used to increase food loss awareness and participation and

to reinforce the message.

Four statements were formulated for the process level:

•The organisation prepares sales forecasts that regularly reflect reality;

•

There is a documented process description that identifies known causes of losses and

highlights avoidance strategies;

•The organisation has clear, formalised procedures for collecting food loss data;

•

The organisation has specific, formalised procedures for dealing with food losses

that occur.

In addition, the following two questions were formulated for the process level, each

with the indicated single-choice options:

•

Which of the following is the most common form used by the organisation for

food losses?

Logistics 2022,6, 61 8 of 23

No storage strategies in place (scale value 1), last-in, first-out (LIFO) (scale

value 3), first-in, first-out (FIFO) (scale value 5), first-expired, first-out (FEFO)

(scale value 7);

•What is the organisation’s storage strategy for perishable products?

Disposal (scale value 1), recovery energy (e.g., via anaerobic digestion) (scale

value 3), recycling food loss into animal feed or composting (scale value 5),

re-using surplus food for human consumption through redistribution networks

or food banks (scale value 7)

The questionnaire also included a section asking which fields of action of FLM the

respondents had already implemented and which of these fields of action should be

addressed in the future [

]. Furthermore, general questions on the demographics of the

respondents were asked.

This online (and anonymous) questionnaire was sent to 171 companies and organisa-

tions involved in the food value chain, during the period 15 January to 28 February 2022.

Forty responses were suitable for analysis and evaluation, which translated to a response

rate of 23.4%. The average response time was 18 min and 25 s. The number of research items

was relatively small; therefore, it cannot be assumed that the sample was representative.

The aim of the study was not to make binding statements for all organisations, but rather

to make initial deductions on the basis of the information found. Further studies can use

this information as a starting point.

3.2. Data Analysis

After the end of the survey period, the data were exported from the Unipark platform.

MS Excel was used for the descriptive analysis, and SPSS, as well as R and R Studio,

were used for further statistical evaluations and analyses and for the application of the

clustering algorithm.

3.2.1. Demographics

Figure 4shows the participation of the different actors in the food value chain.

Logistics 2022, 6, x FOR PEER REVIEW 9 of 23

Figure 4. Value chain actors.

The largest group of participants was logistics service providers, with 11 participants,

followed by manufacturers with 10 participants, import–export businesses with 9 partici-

pants and authorities or NGOs with 6 participants and 4 participants who assigned them-

selves to the group of farmers. There were no representatives from the group of retailers

within the participating organisations. Organisation size was measured by the number of

employees and annual revenue. Both indicators are shown in Figure 5.

Figure 5. Organisation size by employees and annual revenue in USD.

According to the European Union’s definition, 36 of the participating organisations

were small- and medium-sized enterprises [32]. While 5 organisations were micro com-

panies, 10 were small companies and 21 were medium-sized companies.

3.2.2. Cluster Analysis

Clustering is an unsupervised technique for grouping similar objects, whereby the

structure of the data determines the best groups. Unsupervised means that the application

of the chosen algorithm is carried out by the system without external influence. Thus, the

solution path is not necessarily transparent to the applying scientist. The groups therefore

resulted from the chosen algorithm. All observations were evaluated based on their ‘dis-

tance‘ from one another and grouped into clusters. In this case, the proximity or distance

024681012

Authority / NGO

Import/Export Business

Manufacturer

Logistics Service Provider

Farmer

up to 10

51–250

21–50

11–20

501–1.000

251–500

2.501–5.000

5.000–10.000

1.000–2.500

number of employees

1 M–10 M

10 M–50 M

50 M– 100 M

500.000 –1 M

Up to 100.000

100.000–500.000

100 M–500 M

500 M–1 bn

annual revenue

Figure 4. Value chain actors.

The largest group of participants was logistics service providers, with 11 participants,

followed by manufacturers with 10 participants, import–export businesses with 9 par-

ticipants and authorities or NGOs with 6 participants and 4 participants who assigned

themselves to the group of farmers. There were no representatives from the group of

Logistics 2022,6, 61 9 of 23

retailers within the participating organisations. Organisation size was measured by the

number of employees and annual revenue. Both indicators are shown in Figure 5.

Logistics 2022, 6, x FOR PEER REVIEW 9 of 24

Figure 4. Value chain actors.

The largest group of participants was logistics service providers, with 11 participants,

followed by manufacturers with 10 participants, import–export businesses with 9

participants and authorities or NGOs with 6 participants and 4 participants who assigned

themselves to the group of farmers. There were no representatives from the group of

retailers within the participating organisations. Organisation size was measured by the

number of employees and annual revenue. Both indicators are shown in Figure 5.

Figure 5. Organisation size by employees and annual revenue in USD.

According to the European Union’s definition, 36 of the participating organisations

were small- and medium-sized enterprises [32]. While 5 organisations were micro

companies, 10 were small companies and 21 were medium-sized companies.

024681012

Authority / NGO

Import/Export Business

Manufacturer

Logistics Service Provider

Farmer

Figure 5. Organisation size by employees and annual revenue in USD.

According to the European Union’s definition, 36 of the participating organisations

were small- and medium-sized enterprises [

]. While 5 organisations were micro compa-

nies, 10 were small companies and 21 were medium-sized companies.

3.2.2. Cluster Analysis

Clustering is an unsupervised technique for grouping similar objects, whereby the

structure of the data determines the best groups. Unsupervised means that the application

of the chosen algorithm is carried out by the system without external influence. Thus,

the solution path is not necessarily transparent to the applying scientist. The groups

therefore resulted from the chosen algorithm. All observations were evaluated based on

their ‘distance‘ from one another and grouped into clusters. In this case, the proximity

or distance between the observations was calculated using Euclidean distance. For the

distance between the clusters, Ward’s D2 method was used [6,31].

To find commonalities in the cases analysed, an index was formed for each of the five

dimensions presented and surveyed; these indices were used as variables for clustering.

To obtain an overall view of the general engagement with the FLM of an organisation, an

overall index was also calculated from five indices, called the FLM index. The decision

on the number of clusters was made based on the following criteria: The objective of

this research was to identify differences in the ways that organisations address FLM and

to derive recommendations for action on this basis. The aim was not to produce a ‘one

size fits all’ solution, but rather to respond as specifically as possible to the circumstances

identified. Correspondingly, the largest possible number of clusters was found. Based

on these preliminary considerations, the minimum number of clusters was set at three.

In addition, this procedure also considered the fact that the evaluation was based on a

relatively small number of observations. Therefore, it was considered that each cluster

contained enough cases to make a meaningful comparison of the cases. When defining

clusters, there should be at least three cases in each cluster.

With these prerequisites, clustering was carried out in R Studio. Hierarchical clustering

was chosen because it can visually check clusters via a dendrogram. This advantage is also

enhanced by the size of the database. The dendrogram is shown in Figure 6.

Logistics 2022,6, 61 10 of 23

Logistics 2022, 6, x FOR PEER REVIEW 10 of 23

between the observations was calculated using Euclidean distance. For the distance be-

tween the clusters, Ward’s D2 method was used [6,31].

To find commonalities in the cases analysed, an index was formed for each of the five

dimensions presented and surveyed; these indices were used as variables for clustering.

To obtain an overall view of the general engagement with the FLM of an organisation, an

overall index was also calculated from five indices, called the FLM index. The decision on

the number of clusters was made based on the following criteria: The objective of this

research was to identify differences in the ways that organisations address FLM and to

derive recommendations for action on this basis. The aim was not to produce a ‘one size

fits all’ solution, but rather to respond as specifically as possible to the circumstances iden-

tified. Correspondingly, the largest possible number of clusters was found. Based on these

preliminary considerations, the minimum number of clusters was set at three. In addition,

this procedure also considered the fact that the evaluation was based on a relatively small

number of observations. Therefore, it was considered that each cluster contained enough

cases to make a meaningful comparison of the cases. When defining clusters, there should

be at least three cases in each cluster.

With these prerequisites, clustering was carried out in R Studio. Hierarchical cluster-

ing was chosen because it can visually check clusters via a dendrogram. This advantage

is also enhanced by the size of the database. The dendrogram is shown in Figure 6.

Figure 6. Dendrogram of all observations.

Visual examination suggested that three, four or five clusters would be appropriate.

To decide on the number of clusters, an evaluation was made based on 30 indices, all

representing different ways of determining the goodness/quality of the clustering results.

This function calculated 30 indices to determine the number of clusters. According to the

majority rule, this method suggested the best clustering scheme from the different results

obtained by varying all combinations of number of clusters, distance measures and clus-

tering methods. Of the 30 indices, 12 suggested 5 clusters, 9 suggested 4 clusters, and 2

suggested 6 clusters. Five clusters were chosen because all the clusters represented at least

three observations. The distribution of the observations to the clusters is shown in Figure

Figure 6. Dendrogram of all observations.

Visual examination suggested that three, four or five clusters would be appropriate.

To decide on the number of clusters, an evaluation was made based on 30 indices, all

representing different ways of determining the goodness/quality of the clustering results.

This function calculated 30 indices to determine the number of clusters. According to

the majority rule, this method suggested the best clustering scheme from the different

results obtained by varying all combinations of number of clusters, distance measures

and clustering methods. Of the 30 indices, 12 suggested 5 clusters, 9 suggested 4 clusters,

and

2 suggested

6 clusters. Five clusters were chosen because all the clusters represented

at least three observations. The distribution of the observations to the clusters is shown

in Figure 7.

Logistics 2022, 6, x FOR PEER REVIEW 11 of 23

Figure 7. Dendrogram with the allocation of the selected clusters.

The distribution of the number of cases among the clusters can be seen in Table 2.

Table 2. Number of cases per cluster.

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5

8 9 8 3 12

Based on these values, coherent descriptions were developed for all clusters to de-

scribe the characteristics of the organisations within these groups. The results are pre-

sented in the following chapter.

4. Results

In this section, the five identified clusters are explained and the characteristics of the

cases they contain in terms of their engagement with FLM are outlined. As addressed in

the methodology section, Kleineidam et al.’s (2022) [15] proposed model was applied for

the analysis.

The five clusters identified can be viewed in order of increased engagement. Im-

portantly, this does not correspond to the numbering resulting from clustering. Cluster 4,

described above, lies between clusters 1 and 2 in the order of increasing engagement.

In this way, an expert profile was identified showing extensive engagement with

FLM (overall FLM index of 6.1). Likewise, there was advanced engagement with FLM in

the advanced profile (FLM index of 4.3). This was followed by two clusters with interme-

diate levels of engagement that differed greatly in the dimensions of logistics. As a result,

the balanced intermediate profile shows a relatively even development of the logistics

dimensions (FLM index of 3.1), and the area-specific intermediate profile shows advanced

engagement in two dimensions of logistics but little engagement in the other three dimen-

sions (FLM index of 2.7). Finally, the beginner profile describes organisations with little

FLM engagement (FLM index of 1.9).

Table 3 summarises the resulting clusters, with their respective values in the dimen-

sions examined.

Table 3. Dimension specifications of the identified profiles.

Profiles Strategy Network Process Technology Employee FLM Index

Beginner 2.3 1.7 2.6 1.3 1.4 1.9

Area-specific

intermediate 1.2 1.1 1.3 4.6 5.1 2.7

Figure 7. Dendrogram with the allocation of the selected clusters.

The distribution of the number of cases among the clusters can be seen in Table 2.

Table 2. Number of cases per cluster.

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5

8 9 8 3 12

Based on these values, coherent descriptions were developed for all clusters to describe

the characteristics of the organisations within these groups. The results are presented in the

following chapter.

Logistics 2022,6, 61 11 of 23

4. Results

In this section, the five identified clusters are explained and the characteristics of the

cases they contain in terms of their engagement with FLM are outlined. As addressed in

the methodology section, Kleineidam et al.’s (2022) [

] proposed model was applied for

the analysis.

The five clusters identified can be viewed in order of increased engagement. Impor-

tantly, this does not correspond to the numbering resulting from clustering. Cluster 4,

described above, lies between clusters 1 and 2 in the order of increasing engagement.

In this way, an expert profile was identified showing extensive engagement with

FLM (overall FLM index of 6.1). Likewise, there was advanced engagement with FLM

in the advanced profile (FLM index of 4.3). This was followed by two clusters with

intermediate levels of engagement that differed greatly in the dimensions of logistics. As

a result, the balanced intermediate profile shows a relatively even development of the

logistics dimensions (FLM index of 3.1), and the area-specific intermediate profile shows

advanced engagement in two dimensions of logistics but little engagement in the other

three dimensions (FLM index of 2.7). Finally, the beginner profile describes organisations

with little FLM engagement (FLM index of 1.9).

Table 3summarises the resulting clusters, with their respective values in the dimen-

sions examined.

Table 3. Dimension specifications of the identified profiles.

Profiles Strategy Network Process Technology Employee FLM Index

Beginner 2.3 1.7 2.6 1.3 1.4 1.9

Area-specific

intermediate 1.2 1.1 1.3 4.6 5.1 2.7

Balanced

intermediate 3.6 4.2 3.2 2.1 2.3 3.1

Advanced 4.4 4.5 3.7 4.1 4.7 4.3

Expert 6.3 6.1 5.8 5.5 6.6 6.1

3.9 3.9 3.6 3.4 3.9 3.8

Figure 8provides a visualised overview of the characteristics of all dimensions in

the profiles.

The individual profiles and their characteristics are explained in more detail in the

following section.

4.1. Beginner Profile

The beginner profile contained 20% of the participating organisations. These partici-

pants had extremely low levels of engagement with FLM in all dimensions. The strongest

was the process dimension, with an index value of 2.6, followed by strategy with 2.3,

network with 1.7, employees with 1.4 and technology with 1.3.

In this cluster, manufacturers and authorities were equally represented, with three

organisations each, while logistics service providers had two organisations. With the

exception of one small enterprise, all organisations in this group belonged to the cate-

gory of medium-sized enterprises. With six organisations, all participants had a large

number of customers or buyers (>100). Additionally, one organisation stated that it had

20–50 customer

s or buyers, while another stated that it had 50–100. However, regarding

supplier structures, no clear picture could be drawn because 2 organisations stated that they

had fewer than 5 suppliers, 3 organisations stated that they had 20–50, one organisation

stated that they had 50–100 and 2 organisations claimed to have >100 suppliers.

Logistics 2022,6, 61 12 of 23

Logistics 2022, 6, x FOR PEER REVIEW 12 of 23

Balanced inter-

mediate 3.6 4.2 3.2 2.1 2.3 3.1

Advanced 4.4 4.5 3.7 4.1 4.7 4.3

Expert 6.3 6.1 5.8 5.5 6.6 6.1

3.9 3.9 3.6 3.4 3.9 3.8

Figure 8 provides a visualised overview of the characteristics of all dimensions in the

profiles.

Figure 8. Characteristics of the logistics dimensions in the comparison of clusters.

The individual profiles and their characteristics are explained in more detail in the

following section.

4.1. Beginner Profile

The beginner profile contained 20% of the participating organisations. These partici-

pants had extremely low levels of engagement with FLM in all dimensions. The strongest

was the process dimension, with an index value of 2.6, followed by strategy with 2.3, net-

work with 1.7, employees with 1.4 and technology with 1.3.

In this cluster, manufacturers and authorities were equally represented, with three

organisations each, while logistics service providers had two organisations. With the ex-

ception of one small enterprise, all organisations in this group belonged to the category of

medium-sized enterprises. With six organisations, all participants had a large number of

customers or buyers (>100). Additionally, one organisation stated that it had 20–50 cus-

tomers or buyers, while another stated that it had 50–100. However, regarding supplier

structures, no clear picture could be drawn because 2 organisations stated that they had

fewer than 5 suppliers, 3 organisations stated that they had 20–50, one organisation stated

that they had 50–100 and 2 organisations claimed to have >100 suppliers.

Figure 9 shows the specification of the dimensions for this profile. None of the organ-

isations in the beginner profile stated that they had already consciously implemented

measures against food losses, and none planned to implement one or more such measures.

1.0

2.0

3.0

4.0

5.0

6.0

7.0

beginner area specific

intermediate

balanced

intermediate

advanced expert

Strategy Network Process Technology Employee

Figure 8. Characteristics of the logistics dimensions in the comparison of clusters.

Figure 9shows the specification of the dimensions for this profile. None of the

organisations in the beginner profile stated that they had already consciously implemented

measures against food losses, and none planned to implement one or more such measures.

Therefore, the relevance of the topic has yet to be incorporated into the structures of these

organisations, as is clearly reflected in the indices of the logistics dimensions.

Logistics 2022, 6, x FOR PEER REVIEW 13 of 23

Therefore, the relevance of the topic has yet to be incorporated into the structures of these

organisations, as is clearly reflected in the indices of the logistics dimensions.

Figure 9. Level of the five logistics dimensions for the beginner profile.

This was also reflected in the statements these organisations made within the strategy

dimension. Not only did the organisations lack a strategy for dealing with food losses, but

they also stated that their management attached no particular importance to the issue. As

reflected in the other dimensions, no organisation-driven dynamics emerged as a result.

The low levels of the technology dimension and the employee dimension were inevitably

influenced negatively by this aspect. Overall, the statements made by these organisations

show their lack of perceived value in defined and documented processes. The only excep-

tion was the fact that the organisations’ forecasts delivered good results on average.

As part of their FLM, organisations with this profile indicated that they would be

most likely to implement mindfulness measures themselves.

4.2. Area-Specific Intermediate Profile

The area-specific intermediate profile was characterised by an extremely uneven dis-

tribution of indices. The dimensions of network (1.1), strategy (1.2) and process (1.3) were

less pronounced than in the organisations of the beginner profile. The dimensions of tech-

nology (4.6) and employees (5.1), which were the least pronounced in the beginner profile,

were particularly pronounced in this group of organisations. Notably, 8% of the partici-

pating organisations belonged to this cluster.

Only logistics service providers were represented at this level, and they all repre-

sented different company sizes (micro, medium-sized and large enterprises). Despite the

different company sizes, all these organisations stated that they had few customers or cli-

ents (5–10), which could be why they were well positioned in the technology dimension;

they encountered less coordination work in terms of their information technology connec-

tion to customers.

Figure 10 shows the specification of the dimensions for this profile. As in the beginner

profile, none of these organisations had consciously implemented measures against food

losses, and they were not planning to do so in the near future; this indicated that they did

not perceive FLM as a relevant factor within their organisation. However, the high degree

of the technology and employee dimensions reflected a special orientation of the organi-

sations in this category. While they were particularly oriented towards their employees,

the involvement of employees was institutionalised to a high degree through incentive

systems, just as there was a focus on ensuring that employees were regularly trained and

receiving optimal transparency about the organisations’ internal processes. Thus, these

organisations, which to a large extent still lacked problem awareness of FLM, were nev-

ertheless well positioned in the employee dimension, showing that good FLM is not an

isolated action and can be fostered by other factors. Furthermore, these organisations were

characterised by special competences in dealing with technologies. As mentioned previ-

ously, the small number of customers or clients in most of these organisations could make

a positive contribution. However, these organisations were also relatively well positioned

Strategy Network Process Technology Employee

Figure 9. Level of the five logistics dimensions for the beginner profile.

This was also reflected in the statements these organisations made within the strategy

dimension. Not only did the organisations lack a strategy for dealing with food losses,

but they also stated that their management attached no particular importance to the issue.

As reflected in the other dimensions, no organisation-driven dynamics emerged as a

result. The low levels of the technology dimension and the employee dimension were

inevitably influenced negatively by this aspect. Overall, the statements made by these

organisations show their lack of perceived value in defined and documented processes.

The only exception was the fact that the organisations’ forecasts delivered good results

on average.

As part of their FLM, organisations with this profile indicated that they would be most

likely to implement mindfulness measures themselves.

Logistics 2022,6, 61 13 of 23

4.2. Area-Specific Intermediate Profile

The area-specific intermediate profile was characterised by an extremely uneven

distribution of indices. The dimensions of network (1.1), strategy (1.2) and process (1.3)

were less pronounced than in the organisations of the beginner profile. The dimensions

of technology (4.6) and employees (5.1), which were the least pronounced in the beginner

profile, were particularly pronounced in this group of organisations. Notably, 8% of the

participating organisations belonged to this cluster.

Only logistics service providers were represented at this level, and they all represented

different company sizes (micro, medium-sized and large enterprises). Despite the differ-

ent company sizes, all these organisations stated that they had few customers or clients

(

5–10

), which could be why they were well positioned in the technology dimension; they

encountered less coordination work in terms of their information technology connection

to customers.

Figure 10 shows the specification of the dimensions for this profile. As in the beginner

profile, none of these organisations had consciously implemented measures against food

losses, and they were not planning to do so in the near future; this indicated that they

did not perceive FLM as a relevant factor within their organisation. However, the high

degree of the technology and employee dimensions reflected a special orientation of

the organisations in this category. While they were particularly oriented towards their

employees, the involvement of employees was institutionalised to a high degree through

incentive systems, just as there was a focus on ensuring that employees were regularly

trained and receiving optimal transparency about the organisations’ internal processes.

Thus, these organisations, which to a large extent still lacked problem awareness of FLM,

were nevertheless well positioned in the employee dimension, showing that good FLM is

not an isolated action and can be fostered by other factors. Furthermore, these organisations

were characterised by special competences in dealing with technologies. As mentioned

previously, the small number of customers or clients in most of these organisations could

make a positive contribution. However, these organisations were also relatively well

positioned in terms of how new technologies were implemented, showing a reflective

approach with a simultaneous desire to use the benefits of new technologies to their own

advantage as quickly as possible. Organisations in this cluster could therefore be said to

have a high affinity for technologies.

Logistics 2022, 6, x FOR PEER REVIEW 14 of 23

in terms of how new technologies were implemented, showing a reflective approach with

a simultaneous desire to use the benefits of new technologies to their own advantage as

quickly as possible. Organisations in this cluster could therefore be said to have a high

affinity for technologies.

Figure 10. Level of the five logistics dimensions for the area-specific intermediate profile.

4.3. Balanced Intermediate Profile

The balanced intermediate profile contained 23% of the participating organisations.

The FLM index of this profile was minimally higher than that of the area-specific interme-

diate profile but showed a different picture in the expression of the dimensions. The net-

work dimension was the most pronounced in this group, with an index value of 4.2, fol-

lowed by strategy with 3.6, processes with 3.2, employees with 2.3 and technology with

2.1.

Almost half of this group was represented by logistics service providers (four organ-

isations). The rest were distributed as follows: two manufacturers and one farmer, one

import/export company; and one representative of an NGO each. Five participants de-

scribed themselves as medium-sized enterprises, with the remainder claiming to be micro

and small enterprises. The suppliers showed clustering, with 4 participants indicating 5–

10 suppliers, 1 participant indicating 0–5 suppliers, 2 participants indicating 20–50 sup-

pliers and 2 participants indicating >100 suppliers. A similar accumulation was seen with

customers, with 4 participants claiming to have >100 customers, 2 participants each claim-

ing to have 0–5 and 20–50 customers and 1 participant claiming to have 5–10 customers.

Figure 11 shows the specification of the dimensions for this profile. The actors in this

group had limited similarities to the key figures considered here. However, overall, the

organisations with this profile tended to have large networks that they managed well,

given their good performance in the network dimension and their FLM. These organisa-

tions could therefore create and coordinate external processes that function well. Con-

versely, with internal processes, these organisations had low competence in focusing on

their employees, dealing with new technologies, recognising the added value of new tech-

nologies and using them for their own benefit.

Figure 11. Level of the five logistics dimensions for the balanced intermediate profile.

Strategy Network Process Technology Employee

Figure 10. Level of the five logistics dimensions for the area-specific intermediate profile.

4.3. Balanced Intermediate Profile

The balanced intermediate profile contained 23% of the participating organisations.

The FLM index of this profile was minimally higher than that of the area-specific intermedi-

ate profile but showed a different picture in the expression of the dimensions. The network

dimension was the most pronounced in this group, with an index value of 4.2, followed by

strategy with 3.6, processes with 3.2, employees with 2.3 and technology with 2.1.

Almost half of this group was represented by logistics service providers (four or-

ganisations). The rest were distributed as follows: two manufacturers and one farmer,

one import/export company; and one representative of an NGO each. Five participants

described themselves as medium-sized enterprises, with the remainder claiming to be

Logistics 2022,6, 61 14 of 23

micro and small enterprises. The suppliers showed clustering, with 4 participants in-

dicating

5–10 suppliers

, 1 participant indicating 0–5 suppliers, 2 participants indicating

20–50 suppliers and 2 participants indicating >100 suppliers. A similar accumulation

was seen with customers, with 4 participants claiming to have >100 customers, 2 partici-

pants each claiming to have 0–5 and 20–50 customers and 1 participant claiming to have

5–10 customers.

Figure 11 shows the specification of the dimensions for this profile. The actors in this

group had limited similarities to the key figures considered here. However, overall, the

organisations with this profile tended to have large networks that they managed well, given

their good performance in the network dimension and their FLM. These organisations could

therefore create and coordinate external processes that function well. Conversely, with

internal processes, these organisations had low competence in focusing on their employees,

dealing with new technologies, recognising the added value of new technologies and using

them for their own benefit.